Modeling treatment effects on absorbing outcomes in clinical trials: Leveraging longitudinal and ordinal data for efficiency gains.

Randomized clinical trials in critically ill populations often use mortality at the end of follow-up as the primary outcome. When mortality is rare, composite outcomes that capture disease severity over time are frequently employed to improve efficiency. Motivated by the Crystalloid Liberal or Vasopressor Early Resuscitation in Sepsis (CLOVERS) trial-a randomized study comparing two fluid resuscitation strategies in hospitalized patients with sepsis-we evaluate multiple modeling approaches for estimating covariate-adjusted treatment effects on absorbing outcomes such as mortality. While analyses of binary end-of-follow-up outcomes (alive vs. dead) are natural, efficiency can be gained by incorporating low-cost longitudinal and ordinal severity data routinely collected in trials and by imposing reasonable modeling restrictions (constraints). We develop methods that leverage these data within the marginalized model framework, focusing on first-order marginalized transition models, which allows direct estimation of outcome-marginal (population-averaged) treatment effects. We highlight a key property of the marginalized transition model in the presence of absorbing states: the marginal and conditional sub-models correspond to cumulative incidence and discrete hazard models, respectively. Through simulation studies and an application to the CLOVERS data, we compare the validity and efficiency of competing approaches, including standard survival analyses.